Institution
São Paulo Federal Institute of Education, Science and Technology
Education•São Paulo, Brazil•
About: São Paulo Federal Institute of Education, Science and Technology is a education organization based out in São Paulo, Brazil. It is known for research contribution in the topics: Population & Cloud computing. The organization has 1707 authors who have published 2374 publications receiving 11333 citations.
Topics: Population, Cloud computing, Microstructure, Scintillation, Supply chain
Papers
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533 citations
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University of Tartu1, American Museum of Natural History2, University of Gothenburg3, Swedish University of Agricultural Sciences4, University of Oslo5, University of Hawaii at Manoa6, University of Copenhagen7, Purdue University8, Academy of Sciences of the Czech Republic9, University of Turin10, Harvard University11, Synlab Group12, Universidad Santo Tomás13, Universidad Mayor14, University of Electronic Science and Technology of China15, University of Warsaw16, Swedish Museum of Natural History17, Mae Fah Luang University18, University of Florida19, Laos Ministry of Agriculture and Forestry20, São Paulo Federal Institute of Education, Science and Technology21, Estonian University of Life Sciences22, Federal University of Pernambuco23, United States Department of Energy24, Del Rosario University25, National Autonomous University of Mexico26, Ghent University27, West Bengal State University28, Beijing Forestry University29, Pontifical Catholic University of Chile30, Chinese Academy of Sciences31, Field Museum of Natural History32, Leibniz Association33, University of Potsdam34, University of Gilan35, University of Alaska Fairbanks36, University of Tokyo37, University of Costa Rica38, Forest Research Institute39, University of Sydney40, Westmead Hospital41, Uppsala University42, Landcare Research43, University of Chittagong44, University of Memphis45, United Arab Emirates University46, Ministry of Land and Resources of the People's Republic of China47, University of Pretoria48, Royal Botanic Gardens49, Ocean University of China50, Guizhou University51, Mie University52, Hokkaido University53
TL;DR: Fungal traits and character database FungalTraits operating at genus and species hypothesis levels is presented in this article, which includes 17 lifestyle related traits of fungal and Stramenopila genera.
Abstract: The cryptic lifestyle of most fungi necessitates molecular identification of the guild in environmental studies. Over the past decades, rapid development and affordability of molecular tools have tremendously improved insights of the fungal diversity in all ecosystems and habitats. Yet, in spite of the progress of molecular methods, knowledge about functional properties of the fungal taxa is vague and interpretation of environmental studies in an ecologically meaningful manner remains challenging. In order to facilitate functional assignments and ecological interpretation of environmental studies we introduce a user friendly traits and character database FungalTraits operating at genus and species hypothesis levels. Combining the information from previous efforts such as FUNGuild and Fun(Fun) together with involvement of expert knowledge, we reannotated 10,210 and 151 fungal and Stramenopila genera, respectively. This resulted in a stand-alone spreadsheet dataset covering 17 lifestyle related traits of fungal and Stramenopila genera, designed for rapid functional assignments of environmental studies. In order to assign the trait states to fungal species hypotheses, the scientific community of experts manually categorised and assigned available trait information to 697,413 fungal ITS sequences. On the basis of those sequences we were able to summarise trait and host information into 92,623 fungal species hypotheses at 1% dissimilarity threshold.
245 citations
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Chiang Mai University1, Mae Fah Luang University2, Chinese Academy of Sciences3, Ruhr University Bochum4, DSM5, Landcare Research6, Botanic Garden Meise7, University of Tsukuba8, University of Toronto9, New York Botanical Garden10, University of Agriculture, Faisalabad11, Russian Academy of Sciences12, Pierre-and-Marie-Curie University13, Beijing Forestry University14, Ghent University15, University of Amsterdam16, Federal University of Bahia17, Université catholique de Louvain18, Eötvös Loránd University19, West Bengal State University20, University of Miami21, Iranian Research Organization for Science and Technology22, Universidade Federal de Santa Catarina23, Federal University of Pernambuco24, University of Salamanca25, Purdue University26, Sewanee: The University of the South27, University of Pennsylvania28, Hachinohe Institute of Technology29, Clark University30, Seoul National University31, São Paulo Federal Institute of Education, Science and Technology32, Royal Ontario Museum33, University of Gothenburg34, National Museum of Natural History35, American Museum of Natural History36, Federal University of Rio Grande do Norte37, Universidade Federal de Santa Maria38, Instituto Politécnico Nacional39, University of Turin40, Federal University of Paraíba41, University of Tübingen42, Southwest Forestry University43, Royal Botanic Gardens44
TL;DR: Divergence times as additional criterion in ranking provide additional evidence to resolve taxonomic problems in the Basidiomycota taxonomic system, and also provide a better understanding of their phylogeny and evolution.
Abstract: The Basidiomycota constitutes a major phylum of the kingdom Fungi and is second in species numbers to the Ascomycota. The present work provides an overview of all validly published, currently used basidiomycete genera to date in a single document. An outline of all genera of Basidiomycota is provided, which includes 1928 currently used genera names, with 1263 synonyms, which are distributed in 241 families, 68 orders, 18 classes and four subphyla. We provide brief notes for each accepted genus including information on classification, number of accepted species, type species, life mode, habitat, distribution, and sequence information. Furthermore, three phylogenetic analyses with combined LSU, SSU, 5.8s, rpb1, rpb2, and ef1 datasets for the subphyla Agaricomycotina, Pucciniomycotina and Ustilaginomycotina are conducted, respectively. Divergence time estimates are provided to the family level with 632 species from 62 orders, 168 families and 605 genera. Our study indicates that the divergence times of the subphyla in Basidiomycota are 406–430 Mya, classes are 211–383 Mya, and orders are 99–323 Mya, which are largely consistent with previous studies. In this study, all phylogenetically supported families were dated, with the families of Agaricomycotina diverging from 27–178 Mya, Pucciniomycotina from 85–222 Mya, and Ustilaginomycotina from 79–177 Mya. Divergence times as additional criterion in ranking provide additional evidence to resolve taxonomic problems in the Basidiomycota taxonomic system, and also provide a better understanding of their phylogeny and evolution.
233 citations
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TL;DR: This paper runs a cyber-vulnerability assessment, a literature review of the available intrusion detection solutions using ML models, and demonstrates how a ML-based anomaly detection system can perform well in detecting these attacks.
Abstract: It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
230 citations
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TL;DR: The use of patient images and emotional detection to assist patients and elderly people within an in-home healthcare context is discussed and the prototype which runs on multiple computing platforms is outlined, showing results that demonstrate the feasibility of this approach.
195 citations
Authors
Showing all 1736 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew Herod | 32 | 96 | 3706 |
Noé Cheung | 30 | 153 | 2334 |
Laércio Gomes | 26 | 127 | 2337 |
Tiago Carvalho | 24 | 188 | 2198 |
Paulo S. Calefi | 23 | 85 | 1452 |
Leandro C. Peixoto | 22 | 36 | 1136 |
Carlos Frajuca | 18 | 81 | 709 |
Anna Rafaela Cavalcante Braga | 17 | 60 | 779 |
Carlos Roberto Grandini | 17 | 150 | 1325 |
Pedro R. Goulart | 16 | 33 | 919 |
Vagner R. de Mendonça | 16 | 26 | 632 |
Rodolfo I. Meneguette | 15 | 95 | 786 |
Otávio L. Rocha | 14 | 66 | 841 |
Marcelo S. Alencar | 13 | 199 | 870 |
R.C. Souza | 13 | 24 | 618 |